{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "22b6c638",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "from sympy import symbols, cos, sin\n",
    "from sympy.plotting import plot_parametric\n",
    "from math import sin,cos\n",
    "import io\n",
    "import cv2\n",
    "import pykalman\n",
    "from scipy import optimize\n",
    "import matplotlib as mpl\n",
    "from numpy import array, arange, abs as np_abs\n",
    "from numpy.fft import rfft, rfftfreq\n",
    "from math import sin, pi\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import cmath\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43a19f00",
   "metadata": {},
   "source": [
    "# Лабораторная работа 2.4\n",
    "В файле ’Vibration_data.csv’ приведена запись показаний вибродатчика, установленного около четырехцилиндрового четырехтактного двигателя внутреннего сгорания, коленчатый вал которого во время регистрации наблюдений вращался со скоростью 900 оборотов в минуту. Построен амплитудный спектр наблюдаемого сигнала"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "44777108",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('./Vibration_data.csv', sep=\"\\s+\", names=['time', 'value'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "267635f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             time     value\n",
      "0       45.999703  0.175176\n",
      "1       45.999753  0.160528\n",
      "2       45.999803  0.133671\n",
      "3       45.999853  0.084842\n",
      "4       45.999903  0.048219\n",
      "...           ...       ...\n",
      "99996   50.999484  0.068362\n",
      "99997   50.999534  0.090335\n",
      "99998   50.999584  0.068362\n",
      "99999   50.999634  0.039064\n",
      "100000  50.999684  0.012207\n",
      "\n",
      "[100001 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9a4d2be9",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data.time\n",
    "y = data.value\n",
    "N = len(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ea0df57a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0         45.999703\n",
      "1         45.999753\n",
      "2         45.999803\n",
      "3         45.999853\n",
      "4         45.999903\n",
      "            ...    \n",
      "99996     50.999484\n",
      "99997     50.999534\n",
      "99998     50.999584\n",
      "99999     50.999634\n",
      "100000    50.999684\n",
      "Name: time, Length: 100001, dtype: float64\n",
      "0         0.175176\n",
      "1         0.160528\n",
      "2         0.133671\n",
      "3         0.084842\n",
      "4         0.048219\n",
      "            ...   \n",
      "99996     0.068362\n",
      "99997     0.090335\n",
      "99998     0.068362\n",
      "99999     0.039064\n",
      "100000    0.012207\n",
      "Name: value, Length: 100001, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(x)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3ce408fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "T = x[1] - x[0]\n",
    "\n",
    "# график сигнала \n",
    "\n",
    "plt.figure(1)\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "dd4f5215",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f013f522370>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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q4xJs8Tecq3YWpeZ9DqFokOrPUlZZxYaCEjJ90UBDSyUqmZrBOCDPObfeOVcBPAdMjkszGXjS234RGG9m5pz7zDm33du/HOhsZh1bIuMSbPEF2wOzVvHqku11pG66Q6koS3Vcu+25Rby6ZEeNa6NJZxKVTDAYBGzxPd7q7UuYxjkXAgqBPnFpvgQsdM6Vx7+Bmd1gZrlmlpufn59s3iXAEt1wrk5B7UA1g+S9u3o3UDOAqmYgUa3SgWxmY4k0HX070XHn3GPOuRznXE6/fv1aI0uSYok6dlNR1h1CsSDltZzo6/vPmfoMJCqZYLANGOJ7PNjblzCNmWUBPYA93uPBwL+Aa51z65qbYWkbEt3lpmK5hUMqGKT6wyR4edUMJCqZYDAfGGlmw80sG5gCzIxLM5NIBzHAVcA7zjlnZj2B14DpzrmPWijP0ga0VhnTGuv5tJZ0BDYtRyFRDQYDrw/gFmA2sBJ4wTm33MzuM7PLvWRPAH3MLA+4HYgOP70FOBq4y8wWef8Ob/FPIYHTWm35h9KNbTo+i5qJJCormUTOudeB1+P23eXbLgOuTvC8+4H7m5lHaYMSLXOQTHyYtWwHLy/cxmPX5iT1PofSpLOm1HL2l1bwtSfm8fuppzCs72GNfr5GE0mUZiBLSjR1mYMbn17Imyt2JZ3+kKoZNKFcnr18J8u2HeAP7+U1mDZRsCmv1C/QSYSCgaRES7ZF5xeVc6CsMvHBQykYJFHL2V1URnF5qNb+plaQtJqsRCkYSEokbCZqxPP9zT+n/extznng3cTpDqVokIRxP/sPE38zp8VeT78zIVEKBpISzW2Ljh/yWHgwcc3gkGomSvL2fuu+g7Ftw+pJ2TDVDCRKwUBSorlLIye7yukh1YHcjI+SzFMTvb5+m1qiFAwkJZpbM0i2y6E91gxqaF7FgHIFA/EoGATM3pIK3l/T9tdnStQW3ZiyLumawSHUZ9CcT9LUWkVbDwbhsOPfi7drJnULUDAImFufXci0GZ9SWFrH6Jk2orlt0cneJfuTtfUmo6bkP1oxaGpQLA+17aGlf/t4I7c++xkvL9ya7qy0eQoGAbOjsAyAnQfK0pyT5mluW3SyP4ZTMxg06y3TrqH8JzonZsm3EyV6+bbeZ7ByxwFAM6lbgoJBwBzeLfJzD9sLDzaQMtgS3XE25u412Wq/vwbR1ps8dhfVWt29hsoUrCPU1oNBfnHknPU+LDvNOWn7FAwCpkfnDgDsKa5Ic06aLhx2HCire2LU3pIKbnxqAfn1FH7RPoOGmk78Rx95Z22j8xok33lmYb3HE83diElw6NMNe5n+0pJ6z+H+OobsthVF3vesrTcRBoGCQcBkZUQuyff/uZhPN+xNc26apqCkPOEdZ7Qwe/itNcxavpN/L677l8+iN8ENDVFdunV/bHtXG29a89u2v3bNMFEwqK+R6I4XF/Pc/C2syy+uM832BO/TVvz5/XUs2LQP0HyJlqBgEDAZGdV/3r9/t+H1ZlLhmXmb2FBQ0uTnb99fXSgf2btLbDu6RMXGPZHX7tO17qp9tGZQ790wkbWMog7v1qnxmQ0Q/7k698HaM67rayZKdJaO6BE5H8u3R9rVE9097ywsa9ZInI/zCnh31e4mP785fv7Gqth2Q98TaZiCQcD4YgGZzRxD3hQVoTA/+tcyrvnzJ01+jf2lkSauh685kX/fenZsf3TuQbSdv7Si7pEs0c7SxrSTF5dHmjzKQ1Vtpi28rLIqVhh37Vi9iHCiArqxBV52ViZAbC0j/7M/nn4h3z5vBKGwo6SidpNesr7y+Dy++bf5TX5+S9Hqq82nYBAw/vI/M6P1o0G04KhzYbgklHkrYY7u3z3WBwJQUh7ZHy3f73x5KVv2liZ8jWhh6C8AH3prTb3vu98bjnvCPW9y/i8Tr2UUNMf8ZBa3Phup3dS15AZEzsd1TyZf6L6/Jp853nyV4li7evXxgT07M6RXpCZSVk9QbisUDJpPwSBgKn13hI0ZNthSirwgkJ3Z9K9GmTfhrHN2Zo39JV6g8U8oiw4NjBdN4+9kfuQ/a+vtF4gGsvJQmO2Fwe8/CHkF2OtLdwIkXI006u2Vu2LNPf604To62mcu2l4rbbxOHSLXp+wQWKyuQs1EzaZgEDAh3x1OZlqCQaTgiDYxNMVBr2bQqUPNr1eRVyiV+dbQL62oYtayHVz/ZC4P++78w2HHml1FXBK3QueHawuAyDr+B+PuaNtK01BU/Eie+OG4OwvLYoMIPlhbc1b6cXfPBqqDQVllmO89v4hbn/2MZdsK6eBrYyxKMLILoLMXDA62wG8apPvOPKSaQbMl9Utn0nr8k2fS0UwULTg6ZjXtPuFAWSV3vrwUqC5soqI1g2Jf4VRUVsltzy8HIne/UVXOsXlP7Sak372zlgE9OvHtpxbw9TOG1jjW1uYZRPtWIHJnH5//Kx79iJ0Hylj7s0k8PXdzredXhMJEy8BZy3fG9q/YXshJQ3rFHheXhxLO++icHbnG9/57Oc9cf3qzaqL7Syvp582RSYd0B6NDgWoGAeP/Umeksc8gu4nBYMaHG2LbneKCQXF5iIMVVTVmV7+7OvE6TFVhx+YE/Qkb95Sywmta8o946tYxq1bNYE9x/ZO4GmPBpn38+P+WtuhQzFU7i2LbobCrNQM5ep5eXZJ4CO7GPSUJ13Dauu8g2/ZXn7uNBSU1AnAXr/muk1f7+3jdnlh/S1OV1NPE1RrUTNR8CgYBsm3/QT7wmkGg5sii1hLtM2hqzcDftBV9jY+mX8jlJw5kb0kFuZv2UlpRxRPTIr9x/M6q3WRmGBcec3iN1wmH4f7XViZ8j8VbCwHw38h279yB8lBVjSUbTr3/7SZ9hkTuemUZT8/dzDPzNrXYa97yj88AyMqwWK0g0TWfuy7xfJMJD8+ptUTFd84/ivJQmLnr93LikJ5898Kjyd20LxZY7pg4mo9+cCEAnXx9OskuDFiX5oxIagmPf7C+RvOjNJ6CQYD8+f11NR6/smg7O1p5WYrqPoOmfTX8tYFos8Ognp25ZOwRFJWF+PoTnwIwqn+3WLoTB/dgwpj+NV7nC7//MLYdHZF07qh+ACzaEplo5P/j79G5AxWhcIu0fycSLawLilp+ZrhZdX9HfNMawOKt+8nyRYkR/ap/+P7umctrpJ121rDYdv6BMo46vCsAn38kcj4vGXsEvbylG/zv1dwmtuhIsdYyd/2eGo9LK6qY10YnaQaFgkGAZCRos/3Rv5a1ah6izUQdmjCaqLQiVGc/x6TjjqjxeECP6glij087rd4709svHgVAH68Q27I3EiD9k9t6dO5AcXlVs4bE1mX3gTLydkdm8T6fu6VFlkuuihs1Fq2RxTetQaQ5aUDP6vP13xePrvN1e3TuwEs3nQnA9sIyBvToHDs2tE8XjurXNfbY/15FZZXNWsG0tWsGUx6bW2vfwTTXTto6BYMAifYX+Geirs8vTnoFz5YQLUwbu9bLQ2+tYcxds7nv1RUJj2dkGP9zSaQQy8wwsjIz+O6FR/O7qSfT+7BsTvZ1ePq99b1zY6OSsuICjX/Jhh6dO1BQXM5Zv3inUflOxqtLdtR4vNrX1t9U/oK3Kuw475fvAYmDAUBlqPp6XHjM4fz8i8cnTNcxK4NTh/bm9otH8dspJ3HSkJ6xY499PadGWn9AnvibDxj941mN+i0N/3ektBVrBqV1FPqtXTs51CgYBEhpRRUDe3Ti/f85P7Zv455S/jxnfavlIdrRuHDzfv6ZuyXp5z3yn5qLxF10bP9aabp1igxe+95FIwG4fcJovnDiQADGDOzO4rsn1Eh/xUkDGdm/G327Rkap1LcyZffOkdeuK4ZVhMIJh55WhMIs3rK/zteF6iGgj0w9GYDdRXXPYagKOxZu3pfwmH+2sX9sv7+WED8cNypnWK/Y5+/UIYOp446slWbtzybFmua+O34kk08aRHZWBoN7RWoH/tpF5HVqB54/vpf8Eih3vLgktt2aNYOv/GVebPuckX359IfjgbqDhCTnkAgGGwtK+H/PfdbmO5BKykN079yh1hC/3I31t4Wu2H6A7z2/KOFY6z+8l8evZq+u87lLtxYy/tfvxWoE/jHpL8QFg6qw4/01+bE7wnv/vZxh01+jIMGond95BaffNacN4fsTRnH9OSMS5sU/Wxngp1ccB0TuhH951Ql87+JRzPP+8OP17JI4UEQ/++n/+3bC9X6mv7yEyY9+VOdkNudcLNCd7N1l/+btuldHfW7+Zr74h495e8WuWseO+cmsWAF65R8+Svj8aHCM9+BVJzDzls/x12+eFvt+vPbds2ukqatp75nrT+f+K46je6cOtY49fd3pNR537diBzzbvY9j015i1LFIjKq0I1WqjB/jnguoflImOJlq8ZT8XP/R+rNkrnnOO65/M5Z1Vtc/PzsIybnp6Qb0zsQEW+YJ3aUUV3bzPVdLGZ1Jv3lPKd59NXznW5oNBQXE55//qPV5ZtL3NdiA55/j7Jxt5c8WuhHevXTrWPx3k2hnz+Ndn2xIOxXxw1up6F7x7cPYq1uWXsGBj5G5254GyWMfi/I37eM1rIqkKO77+xDymzfiU99bk89nmffz1o40A5CQYtZNoNFLHrExuuXBknU0h8aJ/5GbG1TlD6NQhk/7day9Gt/AnF9fZjv/7d/N4fv5m9pVWJvzBoJcXbgPgL3PWU1IeYmNBCevyi2M1ieF3vh5LGx1Hv2jLfi73Orj3l1awff9BNhaUsPtAGY9/EBla+96amou3RQvLlxZu5f5XV7ApwRwKgJOP7MVfrs2ptb9LdhaDe3XhgtHVo67GDuwR2/7m54YlfD2AoX0O42txczKionMNohZs2suVf/gYiCwCWFoR4if/t5wpj83lhfnVNwcPzlpV43m7DkRuCB6YtYq1u4v5bPP+hO+3v7SSt1fu4lt/y6117MlPNvLGsp08PTf5EVtFZZWx2tQv3ljFrGU7G3hGcN01cxkzF2/nq4/PS8vPeAZ20llZbBZr/QXHz1+v/lI++k4evbtks3FPCZedMKDeSTQ3/D2XT9btYem9lySdp5U7DvDJuj0crKzi5guOTvp5DVmx4wB3vRIZFbLeGzufnZURCwyb95Tw/pp8XsjdQs7QXgzo0YmJxw2IPb/A++2DP763jgevOgGAP7y3jnNH9oul2VlYRt+u2WR5d4+hqjCX//6j2Jh9DFbtPMCnG/byxVMGMXfdHrYXlnHzPxby9spB/OuzbbHX+uZf53Pp8TU7hAFG9e/Kml2RjtamzpF46aaz+NIfP64xgzbeo185hW6dsrh2RmRkUpfsTI7wBYmff/F4fv3m6th5+cFLS2PHXluyg4Licp79dHON5TIe/3ADj/vmSABcefKg2PZdl42hU4dMzh3Vjzlr8lmytZDr/jaf//hW7MzMsNgf8dNzN7N570F2Fh7k9otHsbek+m7X/z53f2EMf/+kepXY3l2yY7WUvl07Mn3SMRzlGz1Ul7suG9NgmkQ6+maa9+2aHTtnUX+Zs4GXvJ+UvOOlJfzvGyv5xlnD+MN7kZFvU8cN4f3V+cz4aANTThsSG0Bw36srOHlIT3559Ymx1yopD9VYfuT9NfnsL61g8kmDOFhRxSvedyx+BN1jc9ZxWMcs3l21m+9fUrPz/OjDu9b4O7/x6QVs+PmlLbaUS3moivv+vYJTh/birKP6xlaCbciBskpOuOdNfnrFcbUmR/oVl4d4d9VuenTuwHvenJsFm/Yxf+NezhjRp973KC4P0SkrI/Y33VyWTEehmU0EfgtkAo87534Rd7wj8HfgVGAPcI1zbqN37E7gOqAK+K5zbnZ973XqqTku57Y/MXv5Lgb06MQndyZuFgDI213ETU8vZO3uutdr//zxA1i8dT83nX8UXz09clE27ynlXG8hsz997RT2llTy5ZzBNU5qOOwIhR1V3qqO76zczR0vVbeRrvrpRJZvL2TxlkKuPXMo767O593Vu7n5gqMZ2KMT5aEwE38zh417SnnyW+M4bmB3/jFvM/sPVjL+mMMpragiK9OYvXwnzsFz3l3XhccczoxvnEbe7mL2FJdzTYJREwC3XHA054zsy8Nvr2Hu+uoaUfdOWQl/WAYiY9DvmHgMj3+wvtYY/iG9O8dG6dx20Uie/Hgj+xo5Eemp68bFho5u/MXnG/XcqEVb9nPFox/Rs0sHFt01od60uw+U8enGvVx2wkDKQ1Us2VpIdmYGJw7pycUPvV/v96Kx/nnjmZw2rDd3vLiYF3Jb7vd2H/zSCVydM5g5awvoc1g2Ywd2pyrseHHBVq48ZVCNwjqRYdNfA5p+vtfuKuLih+dwWHYmV+cM4W8fb2zU87997gg+zCuIrZvUrVNWreUvnrn+dM46qg9f+P2HLNtWey2qzh0yaw0J/toZRzL+2P68uGBrrHYa75nrT2f0EZE+peh5ALj38rGs2nmAyScNIsOMvN3FbN9/kL5ds7ni5EG8sWwnd768lK+dcSQ/nXwc5aEw6/KLefitNfzw0mPp2SWbF3K3MHHsEazZVcQNTy2Ivfaz/3UGQ/t0oU/XbDLNyMywGoFnb0kFs5btZOu+0ljAXHrPBLp1isyDuf/VlXywNp++XTuSuylx3xLA1HFHcsclo2PDgOOFw44RP3ydrAzjurOHc+elx2JmC5xztauVSWowGJhZJrAGuBjYCswHpjrnVvjSfAc4wTl3o5lNAa50zl1jZmOAZ4FxwEDgbWCUc67ORrGOA0a6AdN+E3s8cewRlFSE+Gzzfkb170pxeYg1u4rp0blDg22LjXXtmUM5sncXXlm0naXbClv0tZPx3xeP4tbxI2vs83/JW8vq+ydyyn1vNaoN9sqTB/HQl0+MNas0tXD6KK+Arz4+j3NG9uWpuPbsxjj7gXfYuq/hORrHD+rBKzd/jpueWcDs5ZF27MG9Otd67oIfX0Sfrh15f00+07waSSI/nTyWr50xtEbzUn3uvXxsjbkBjdXcYLB6Z2T9pzNH9OEv03I48d43G9VEcfMFR3HB6MO56k/1L3neJTuz3iXLG+uYI7ox67ZzY48rQmGu+tPHLNnauL9bs+b/dvaEMf05fUQfFm7eV2fgaqpOHTIoqwwzun83srMy2LqvlJxhvSkqq6xxEwiw6YHLmhUMkqlfjAPynHPrnXMVwHPA5Lg0k4Enve0XgfEWCZeTgeecc+XOuQ1Anvd6SZu1fCcfrC2guDzEws37Y80Q/kBw9xfGsODHF/HdC4/m8Wtz+NIpg2t+SKs5Uacuf/9kE/e/trJFA8F5o/o1mCZatb7x/KNqHXvpprMAOHVoL070OjCfum4cP7r02BrpHpl6MiP61v6MWRnG4rsncPMFR3HcoO4cll3/nebYgd3pmJXJbRdFxvZ/5/yjmD7pGAA+d3QfzhzRh1/5qv4A472hjmbGq7eeHRt10xSnDu3FV04/koe+fFKTXwOIfQd+edUJzPmfCwD4wcRjWHLPBL530ShevfVs/uuc4fx2yklkZBinD49UyX/8+WN5+/bz+Ns3T4u91rwfjqePN6LpvFH9+O2U6rzNvXM88344ns8fH2m2yxnWGzPjr988jXsvH8uMb+Twk8vGsP5/L+XUoZHhs2t/NonLToikP9E39LMpXrrpLL7nXaumGNW/K3dOOoZHv3oKXTtm8c5/nxf7HW6AM0f04ZufG8a3Pjc8tu+RqSdz7qh+ZGUYZ47oy/GDeyR66Rr9Rh2zIjW2h685kX/fcnbC9BPG9Oc7vr+BUf278uPPH8tvrjkJiIwcGtI7MjLq2f86o8Zzs7MyuG9yZMDB4UmskXTykT2B5gcCgDdX7OKnr65IKhCMHdi91r4bzzuKR6aezA8mHkPezyaRM7R6mHV01NnqXUUs3VbIvtJK3lqxq1YgaAnJ1AyuAiY65673Hn8dON05d4svzTIvzVbv8TrgdOAeYK5z7mlv/xPAG865F+Pe4wbgBoC+g4afunvLOvaWVPDOqt10zs6kV5dsOnXIZPv+gxzZuwvrC4oZ3KsLYwd2Z86aAi4Z2z9hG+Eri7Zx2rDeDOzZmVBVmIqqMNv2HaSoPMRJg3tiFhmBsGL7AfaWlFMeChN2jvLKMBeN6c/BiiqG9O5CVdhx4GAlPTp34K2VuxjdvxuLt+5ncK/OjOjblV1FZRxzRPdYP8eOwjI+zCsgO9P4cs4QSiuq+DCvgDEDurN2dxHnjOxHVdiRYUZ2VgZVYcfekopGL/RVFXZs3VfKkb27xD7/ml1F7DpQxuHdOnFEj061RujsLamgpDzEkN5dCFWFWbRlP70Py6Z75w44FxnRk2j2cVllVa1JSgfKQgzq2blW2iCILvwWzfOOwoMc3q1TnZPiQlVhtu47yLC4gFpUVhnryPa/dnF5qMZ+5xzrC0pqTOqKV1oRWZspGljiz2nQ5O0uZnjfw2qcs7ryXFweoirsKCytJCMj8hOkpw7tDURqH8P6dqnV5FVYWsn6gmL2FFdw4pCese+/c44NBSWMqOdc1mX3gTIO9/qPSitCZGdmEAo73lyxi5yhvVizq4jPHd0XIzIq7LThvenfvSMjD+9GZVXk+7Jg0z7GDuzOx+sKKCoLceLgnuwoLOP04b0pKgvR1RsivX3/QZyDN1fs9IbvGgcrQxxzRHeO6teVDplGRVWYTzfsZfQR3ejUIZNuHbMwMz7bvI/yUDhhv0BZZRXvrtrNxOOO4NUlO+h9WDbZWRnsLCxj9BHdWLatkC7ZWVRWhbl4TH8yM4wP1xZw4bH9U95MlPJg4JeTk+Nyc2uPNBARkbo1t88gmWaibcAQ3+PB3r6EacwsC+hBpCM5meeKiEiaJRMM5gMjzWy4mWUDU4CZcWlmAtO87auAd1ykyjETmGJmHc1sODASqLv3TURE0qLBeQbOuZCZ3QLMJjK0dIZzbrmZ3QfkOudmAk8AT5lZHrCXSMDAS/cCsAIIATfXN5JIRETSI6l5Bq1JfQYiIo3XGn0GIiJyiFMwEBERBQMREVEwEBERAtiBbGZFQN0L8AdHX6CgwVTpp3y2LOWz5bSFPELbyedo51y3hpMlFsQlrFc3p0e8tZhZrvLZcpTPltUW8tkW8ghtK5/Neb6aiURERMFARESCGQweS3cGkqR8tizls2W1hXy2hTxCO8ln4DqQRUSk9QWxZiAiIq1MwUBERNIbDMzsajNbbmZhM8uJO3anmeWZ2Wozu8S3f6K3L8/Mpqchz8+b2SLv30YzW+TtH2ZmB33H/tTaeYvL5z1mts2Xn0t9xxKe2zTk8ZdmtsrMlpjZv8ysp7c/UOfSy1Nav3d1MbMhZvauma3w/pb+n7e/zuufxrxuNLOlXn5yvX29zewtM1vr/d+roddJcR5H+87ZIjM7YGa3BeF8mtkMM9vt/ZhYdF/C82cRj3jf1yVmdkqDb+CcS9s/4FhgNPAekOPbPwZYDHQEhgPriCyfneltjwCyvTRj0pj/XwN3edvDgGXpPJ9xebsH+H6C/QnPbZryOAHI8rYfAB4I6LkM1PcuLm8DgFO87W7AGu8aJ7z+ac7rRqBv3L4Hgene9vTodyAI/7zrvhMYGoTzCZwLnOL/26jr/AGXAm8ABpwBzGvo9dNaM3DOrXTOJZptPBl4zjlX7pzbAOQB47x/ec659c65CuA5L22rs8iPDn8ZeDYd798MdZ3bVuece9M5F/IeziXyS3hBFJjvXTzn3A7n3EJvuwhYCQxKb64aZTLwpLf9JHBF+rJSy3hgnXNuU7ozAuCcm0Pk92L86jp/k4G/u4i5QE8zG1Df6we1z2AQsMX3eKu3r6796XAOsMs5t9a3b7iZfWZm75vZOWnKl98tXhVxhq/6HaRz6PctIncyUUE6l0E9ZzWY2TDgZGCetyvR9U8nB7xpZgvM7AZvX3/n3A5veyfQPz1ZS2gKNW/2gnY+oe7z1+jvbMqDgZm9bWbLEvwLxJ1VIknmeSo1vyg7gCOdcycDtwP/MLPuacznH4GjgJO8vP06lXlpYh6jaX5E5JfwnvF2tfq5bOvMrCvwEnCbc+4AAbn+cc52zp0CTAJuNrNz/QddpH0jEGPdLfITv5cD//R2BfF81tDc85fytYmccxc14WnbgCG+x4O9fdSzv8U0lGczywK+CJzqe045UO5tLzCzdcAoIGU/25bsuTWzvwCveg/rO7ctLolz+Q3gMmC892VOy7lsQKues8Yysw5EAsEzzrmXAZxzu3zH/dc/bZxz27z/d5vZv4g0v+0yswHOuR1eM8butGay2iRgYfQ8BvF8euo6f43+zga1mWgmMMXMOprZcGAk8CkwHxhpZsO9yD3FS9vaLgJWOee2RneYWT8zy/S2R3h5Xp+GvEXz428fvBKIjkCo69y2OjObCNwBXO6cK/XtD9S5JDjfu1q8vqsngJXOuYd8++u6/mlhZoeZWbfoNpHBA8uInMdpXrJpwCvpyWEtNWr+QTufPnWdv5nAtd6oojOAQl9zUmJp7h2/kkhbVjmwC5jtO/YjIiM4VgOTfPsvJTJiYh3wozTl+2/AjXH7vgQsBxYBC4EvpPncPgUsBZZ4X4wBDZ3bNOQxj0i75iLv35+CeC6D8r2rI19nE2kaWOI7j5fWd/3TlM8RREZhLfau7Y+8/X2A/wBrgbeB3gE4p4cBe4Aevn1pP59EgtMOoNIrN6+r6/wRGUX0qPd9XYpvtGZd/7QchYiIBLaZSEREWpGCgYiIKBiIiIiCgYiIoGAgIiIoGIiICAoGIiIC/H/JlL2CIpmOnQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fy = abs(np.fft.fftshift(np.fft.fft(y) * T))\n",
    "Fs = 1 / T \n",
    "d = Fs / N\n",
    "fx = [d * p for p in range(int(-N / 2 - 1 ), int(N / 2))]\n",
    "# График амплитуды сигнала\n",
    "\n",
    "plt.figure(1)\n",
    "plt.xlim(-100, 100)\n",
    "plt.plot(fx, fy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eec089a1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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